{"id":"W2088713545","doi":"10.1073/pnas.1420903112","title":"Chemodetection in fluctuating environments: Receptor coupling, buffering, and antagonism","year":2015,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Human Frontier Science Program; Simons Foundation","keywords":"Antagonism; Receptor; Computational biology; Biology; Coupling (piping); Immune system; Ligand (biochemistry); Biological system; Chemistry; Ecology; Computer science; Cell biology; Immunology; Biochemistry; Materials science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002403867,0.00007477268,0.0001094699,0.0001607154,0.00008630015,0.0000446,0.0007159074,0.00004436177,9.666468e-7],"category_scores_gemma":[0.0006118244,0.00005798161,0.00002493069,0.0005998006,0.0003194371,0.0009688445,0.000359084,0.0001204067,6.666526e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008282028,"about_ca_system_score_gemma":0.00003880665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000849068,"about_ca_topic_score_gemma":3.739898e-8,"domain_scores_codex":[0.9983967,0.000008256727,0.0002678987,0.0002628648,0.0009486621,0.000115631],"domain_scores_gemma":[0.999473,0.000131059,0.0002778465,0.000008602647,0.00007238285,0.000037119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001819205,0.00009219941,0.01243698,0.00005846256,0.00001243805,1.040144e-8,0.001951954,0.04793002,0.7352397,0.1949959,0.0001461319,0.007118003],"study_design_scores_gemma":[0.0002792867,0.00004846114,0.05483106,0.00005946558,0.000002138442,0.000007964162,0.0001537677,0.4998447,0.3094136,0.1351065,0.0001473553,0.0001056529],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963689,0.00009205216,0.001091247,0.001370159,0.00004310583,0.0001177911,0.000001649647,0.00001092972,0.0009041883],"genre_scores_gemma":[0.961562,0.00001246157,0.0382739,0.00008509983,0.00003357441,0.000005819093,4.245616e-8,0.000002213005,0.00002492044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4519147,"threshold_uncertainty_score":0.2364421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05715096600561997,"score_gpt":0.3209870794408756,"score_spread":0.2638361134352556,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}